fixes to make pdf build

_book/css/style.css

@@ -19,7 +19,7 @@
   background-image: url("../images/warning.png");
 }
 p.caption {
-  color: #777;
+  color: #454444;
   margin-top: 10px;
 }
 p code {

bibliography/protected_species_hotspots.bib

@@ -9,7 +9,7 @@ @article{Gende2006
   publisher={Elsevier}
 }
 
-@article{@White2020,
+@article{White2020,
   title={Spatial ecology of long-tailed ducks and white-winged scoters wintering on Nantucket Shoals},
   author={White, Timothy P and Veit, Richard R},
   journal={Ecosphere},
@@ -20,7 +20,7 @@ @article{
   publisher={Wiley Online Library}
 }
 
-@misc{@Palka2017,
+@misc{Palka2017,
   title={Atlantic Marine Assessment Program for Protected Species: 2010--2014 US Dept. of the Interior, Bureau of Ocean Energy Management, Atlantic OCS Region, Washington, DC. OCS Study BOEM 2017-071},
   author={Palka, DL and Chavez-Rosales, S and Josephson, E and Cholewiak, D and Haas, HL and Garrison, L and Orphanides, C},
   year={2017}

chapters/Catch_and_Fleet_Diversity_indicators.Rmd

@@ -38,7 +38,7 @@ spp <- spp %>%
   dplyr::select(Group, NESPP3, 'Common Name', 'Scientific Name')
  
 knitr::kable(spp, caption="Species grouping", booktabs=T, longtable = T) %>%
-  kableExtra::kable_styling(full_width = T,  latex_options = c("repeat_header"), font_size = 8) %>%
+  kableExtra::kable_styling(latex_options = c("repeat_header"), font_size = 8) %>%
   kableExtra::collapse_rows(columns = 1)
 
 ```

chapters/Species_density_estimates.Rmd

@@ -27,7 +27,7 @@ Current and historical species distributions are based on the NEFSC Bottom Trawl
 ### Data analysis
 
 Code used for species density analysis can be found [here](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/stored_scripts/species_density_analysis.R). 
-```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/tech-doc/master/R/stored_scripts/species_density_analysis.R"), eval=F, echo=T}
+```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/tech-doc/master/R/stored_scripts/species_density_analysis.R"), eval=F, echo=F}
 
 ```
 

chapters/Zooplankton_indicators.Rmd

@@ -23,7 +23,7 @@ Zooplankton data are from the National Oceanographic and Atmospheric Administrat
 Data are from the publicly available zooplankton dataset on the NOAA File Transfer Protocol (FTP) server. The excel file has a list of excluded samples and cruises based on @Kane2007 and @Kane2011.
 
 R code used in extraction process.
-```{r, echo = T, eval = F}
+```{r, echo = F, eval = F}
 # load data
 URL='ftp://ftp.nefsc.noaa.gov/pub/hydro/zooplankton_data/EcoMon_Plankton_Data_v3_0.xlsx'
 ZPD=openxlsx::read.xlsx(URL, sheet='Data')

chapters/cold_pool_index.Rmd

@@ -53,7 +53,8 @@ The Cold Pool Index (Model_CPI) was adapted from Miller et al. (2016). Residual
 
 
 
-$${{Model}__CPI}_y=\ \frac{\sum_{i=1}^{n}{{(T}_{i,\ y}\ -\ {\bar{T}}_{i,\ 1972-2019})\ }}{n}$$
+$${{CPI}_y}=\ \frac{\sum_{i=1}^{n}{{(T}_{i,\ y}\ -\ {\bar{T}}_{i,\ 1972-2019})\ }}{n}$$
+
 
 
 
@@ -62,23 +63,23 @@ where n is the number of grid cells over the Cold Pool domain.
 
 #### Persistence Index (Model_PI)
 
-The temporal component of the Cold Pool was calculated using the persistence index (Model_PI). Model_PI measures the duration of the Cold Pool and is estimated using the month when bottom temperature rises above 10˚C after the Cold Pool is formed each year. We first selected the area over the cold pool domain in which bottom temperature falls below 10˚C between June and October. We then calculated the “residual month” in each grid cell, i, in the Cold Pool domain as the difference between the month when bottom temperature rises above 10˚C in year y and the average of those months over the period 1972–2019. Then, Model_PI was calculated as the mean “residual month” over the Cold Pool domain:
+The temporal component of the Cold Pool was calculated using the persistence index (Model_PI). Model_PI measures the duration of the Cold Pool and is estimated using the month when bottom temperature rises above 10C after the Cold Pool is formed each year. We first selected the area over the cold pool domain in which bottom temperature falls below 10C between June and October. We then calculated the “residual month” in each grid cell, i, in the Cold Pool domain as the difference between the month when bottom temperature rises above 10C in year y and the average of those months over the period 1972–2019. Then, Model_PI was calculated as the mean “residual month” over the Cold Pool domain:
 
 
 $${PI}_y=\ \frac{\sum_{i=1}^{n}{{(Month}_{i,\ y}\ -\ {\bar{Month}}_{i,\ 1972-2019})\ }}{n}$$
 
 
 #### Spatial Extent Index (Model_SEI)
 
-The spatial component of the Cold Pool and the habitat provided by the cold pool was calculated using the Spatial Extent Index (Model_SEI). The Model_SEI is estimated by the number of cells where bottom temperature remains below 10˚C for at least 2 months between June and September. 
+The spatial component of the Cold Pool and the habitat provided by the cold pool was calculated using the Spatial Extent Index (Model_SEI). The Model_SEI is estimated by the number of cells where bottom temperature remains below 10C for at least 2 months between June and September. 
 
 The Bottom temperature data are from ROMS-NWA between 1958 and 1992, from Glorys reanalysis between 1993 and 2019 and from Global Ocean Physics for 2020 and 2021.
 
 Bottom temperature from Glorys reanalysis and Global Ocean Physics Analysis were not being processed. 
 
-Bottom temperature from ROMS-NWA (used for the period 1958-1992) were bias-corrected. Previous studies that focused on the ROMS-NWA-based Cold Pool highlighted strong and consistent warm bias in bottom temperature of about 1.5˚C during the stratified seasons over the period of 1958-2007 (Chen et al., 2018; Chen and Curchitser, 2020). In order to bias-correct bottom temperature from ROMS-NWA, we used the monthly climatologies of observed bottom temperature from the Northwest Atlantic Ocean regional climatology (NWARC) over decadal periods from 1955 to 1994. The NWARC provides high resolution (1/10° grids) of quality-controlled in situ ocean temperature based on a large volume of observed temperature data (Seidov et al., 2016a, 2016b) (https://www.ncei.noaa.gov/products/northwest-atlantic-regional-climatology). The first step was to re-grid the ROMS-NWA to obtain bottom temperature over the same 1/10° grid as the NWARC. Then, a monthly bias was calculated in each grid cell and for each decade (1955–1964, 1965–1974, 1975–1984, 1985–1994) in the MAB and in the SNE shelf:
+Bottom temperature from ROMS-NWA (used for the period 1958-1992) were bias-corrected. Previous studies that focused on the ROMS-NWA-based Cold Pool highlighted strong and consistent warm bias in bottom temperature of about 1.5C during the stratified seasons over the period of 1958-2007 (Chen et al., 2018; Chen and Curchitser, 2020). In order to bias-correct bottom temperature from ROMS-NWA, we used the monthly climatologies of observed bottom temperature from the Northwest Atlantic Ocean regional climatology (NWARC) over decadal periods from 1955 to 1994. The NWARC provides high resolution (1/10° grids) of quality-controlled in situ ocean temperature based on a large volume of observed temperature data (Seidov et al., 2016a, 2016b) (https://www.ncei.noaa.gov/products/northwest-atlantic-regional-climatology). The first step was to re-grid the ROMS-NWA to obtain bottom temperature over the same 1/10° grid as the NWARC. Then, a monthly bias was calculated in each grid cell and for each decade (1955–1964, 1965–1974, 1975–1984, 1985–1994) in the MAB and in the SNE shelf:
 
-$${BIAS}_{i,\ d}=\ T_{i,d}^{Climatology}\ -\ {\bar{T}}_{i,\ d}^{ROMS-NWA}\$$
+$${BIAS}_{i,\ d}=\ T_{i,d}^{Climatology}\ -\ {\bar{T}}_{i,\ d}^{ROMS-NWA}$$
 
 
 where $$T_{i,d}^{Climatology}$$ is the NWARC bottom temperature in the grid cell i for the decade d and $${\bar{T}}_{i,\ d}^{ROMS-NWA}$$ is the average ROMS-NWA bottom temperature over the decade d in the grid cell i. 
@@ -144,7 +145,7 @@ Chen, Z., and Curchitser, E. N. 2020. Interannual Variability of the Mid‐Atlan
 
 Fernandez, E., and Lellouche, J. M. 2018. Product user manual for the global ocean physical reanalysis product GLORYS12V1. Copernicus Product User Manual, 4: 1–15.
 
-Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., et al. 2018. Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1∕12° high-resolution system. Ocean Science, 14: 1093–1126.
+Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., et al. 2018. Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 112° high-resolution system. Ocean Science, 14: 1093–1126.
 
 Miller, T. J., Hare, J. A., and Alade, L. A. 2016. A state-space approach to incorporating environmental effects on recruitment in an age-structured assessment model with an application to southern New England yellowtail flounder. Canadian Journal of Fisheries and Aquatic Sciences, 73: 1261–1270.
 

chapters/forage_energy_density.Rmd

@@ -30,8 +30,8 @@ forage.tab <- data.frame('Common Name' = c('Atlantic Herring','alewife','silver
 names(forage.tab) <- c("Common Name","Scientific Name")
 
 
-knitr::kable(forage.tab, caption = "List of forage fish study species.",  booktabs=T) %>%
-  kableExtra::kable_styling(full_width = F)
+knitr::kable(forage.tab, caption = "List of forage fish study species.",  booktabs=T) #%>%
+ # kableExtra::kable_styling(full_width = F)
 ```
 
 

chapters/habs_psp.Rmd

@@ -1,4 +1,4 @@
-# Harmful Algal Blooms - Paralitic Shellfish Poisoning Indicator
+# Harmful Algal Blooms - Paralytic Shellfish Poisoning Indicator
 
 **Description**: Paralytic Shellfish Poisoning (PSP) toxins in the Gulf of Maine
 

chapters/hms_landings.Rmd

@@ -36,7 +36,7 @@ Price per pound was used to determine the ex-vessel value. For landings with pri
 High migratory landings include 26 species of tunas, sharks and swordfish.
 
 Data were processed and analyzed using SAS and Microsoft Excel pivot tables. 
-The count of dealers and vessels in each regional species/management group sum was used to determine if a sufficient number of records were available to make the data public or if it needed to be marked as confidential. Additionally, ratios of landings reported by dealers/fishermen were compared in each regional species/managment group sum to determine if any one entity cotnributed more than ⅔ of the total which would require it being marked as confidential.
+The count of dealers and vessels in each regional species/management group sum was used to determine if a sufficient number of records were available to make the data public or if it needed to be marked as confidential. Additionally, ratios of landings reported by dealers/fishermen were compared in each regional species/managment group sum to determine if any one entity contributed more than 2/3 of the total which would require it being marked as confidential.
 
 ### Data Processing 
 

chapters/landings_data.Rmd

@@ -24,8 +24,8 @@ Fisheries dependent data for the Northeast Shelf extend back several decades. Da
 ```{r calibration1, eval = T, echo = F}
 com.tables <- data.frame(Table = c('WOLANDS', 'WODETS', 'CFDETS_AA'),
                          Years = c('1964 - 1981', '1982 - 1993', '> 1994'))
-knitr::kable(com.tables, caption="Data formats", booktabs = T) %>% 
-  kableExtra::kable_styling(full_width = F)
+knitr::kable(com.tables, caption="Data formats", booktabs = T) #%>% 
+  #kableExtra::kable_styling(full_width = F)
 
 ```
 
@@ -50,8 +50,8 @@ gear.table <- data.frame('gear code' = c(1,2,3,4,5,6,7,8,9),
 names(gear.table) <- c("","Major gear")
 
 
-knitr::kable(gear.table, caption = "Gear types used in commercial landings",  booktabs=T) %>%
-  kableExtra::kable_styling(full_width = F)
+knitr::kable(gear.table, caption = "Gear types used in commercial landings",  booktabs=T)# %>%
+  #kableExtra::kable_styling(full_width = F)
 ```
 
 Several species have additional steps after the data is pulled from CFDBS.  Skates are typically landed as a species complex.  In order to segregate the catch into species, the ratio of individual skate species in the NEFSC bottom trawl survey is used to disaggregate the landings. A similar algorithm is used to separate silver and offshore hake which can be mistaken for one another.  Finally, Atlantic herring landings are pulled from a separate database as the most accurate weights are housed by the State of Maine.  Comlands pulls from the State database and replaces the less accurate numbers from the federal database.

chapters/long_term_sst_indicator.Rmd

@@ -33,8 +33,8 @@ df <- data.frame(
 )
 
 knitr::kable(df,
-             caption="Coordinates used in NOAA ERSST V5 data extraction.",  booktabs=T) %>%
-  kableExtra::kable_styling(full_width = F) 
+             caption="Coordinates used in NOAA ERSST V5 data extraction.",  booktabs=T) #%>%
+  #kableExtra::kable_styling(full_width = F) 
 ```
 
 R code used in extracting time series of long-term SST data can be found [here](https://github.com/NOAA-EDAB/tech-doc/tree/master/R/stored_scripts/long-term-sst-extraction.R).

chapters/wind_habitat_occupancy.Rmd

@@ -40,6 +40,20 @@ Code used to build the [table](https://github.com/NOAA-EDAB/ecodata/blob/master/
 
 ```
 
-```{r wind-map,code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/human_dimensions_MAB.Rmd-wind-map.R"), fig.cap="Map of BOEM existing (black) and proposed (red) lease areas as of February 2019.", message=FALSE, results=FALSE}
+<!-- # ```{r wind-map,code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/human_dimensions_MAB.Rmd-wind-map.R"), fig.cap="Map of BOEM existing (black) and proposed (red) lease areas as of February 2019.", message=FALSE, results=FALSE} -->
+<!-- #  -->
+<!-- # ``` -->
+
+
+```{r wind-map, fig.cap="Map of BOEM existing (black) and proposed (red) lease areas as of February 2019.", message=FALSE, results=FALSE}
+
+image.dir <- here::here("images")
+
+knitr::include_graphics(file.path(image.dir, "wind_hab_occupancy.png"))
+
+```
+
+
+
+
 
-```